1/Muse is changing the dynamics of automotive advertising. We analyzed 654 cases to assess whether Muse could help Meta further expand its monetization opportunities. Meta has long lacked direct purchase-intent signals, particularly data indicating when consumers are ready to buy. As a result, its share of advertising spending in industries such as travel, automotive, B2B and local services, and finance has been much smaller than Google’s. For example, when shopping for a car, you might compare the Model X and Model Y on Google, but not on Meta’s platforms. Much of the associated automotive ad revenue therefore flows to Google. With the emergence of Muse, however, this is beginning to change. In one of the cases we reviewed, a user shopping for a car delegated several tasks to Muse that would otherwise have required separate efforts: researching vehicles, negotiating with dealers, finding insurance quotes, and scheduling a pre-purchase inspection. The user’s account covered vehicle selection, price negotiations, and outreach to insurers and vehicle inspection providers. This significantly changes the dynamics of automotive advertising: -A consumer researching their first EV may compare models on Google or ChatGPT, then visit automakers’ websites. -When the consumer later opens Facebook or Instagram, advertisers can use CRM data to retarget them, encourage them to book a test drive, and move them toward a purchase. -Google and Meta earn ad revenue at different stages of this journey. If Muse handles the earlier research and comparison work, Meta could monetize those stages in addition to the conversion activity it already captures. This would allow Meta to capture value further upstream in the purchase journey. Detailed Report fundaai.substack.com/p/deepm…

FUNDA Play’s monitoring data is consistent with the upward pricing trend at NBIS. H100 pricing has stabilized, while prices for all other instance types continue to rise. Over the past month, NeoLab has been the largest marginal buyer. Having raised funding just a few months ago, they now need to lock in capacity. Beyond NeoLab, robotics companies are also emerging as marginal buyers. However, demand from NeoLab appears less sustainable than demand from robotics companies, which is somewhat concerning. Subscribe to FUNDA’s Substack to get full access to the FUNDA Platform.substack.com/@fundaai

This also marks the second price adjustment for $NBIS ’s on-demand offering. Prices increased by 30% in June and by 20% this time, totaling 50% across the two hikes.

Twitter is once again consumed by the debate over slowing down AI. Anthropic is calling for slower development of frontier models and stronger safety oversight. We had a similar discussion a few weeks ago. This time, though, Dario has laid out a serious, detailed argument in a lengthy essay, and both Sam and Elon have expressed support. There’s plenty of skepticism. The most common argument is that training models has become too expensive, and frontier labs want an excuse to slow down. Or perhaps they want to let the market catch up with model capabilities, then ramp up training again once demand grows. An important distinction here is that slowing down releases does not necessarily mean slowing down training. Both Anthropic and OpenAI have developed advanced internal models that are not publicly available. And the safety risks AI poses to people vary substantially across industries. In some fields, AI is rapidly dismantling organizational structures and ways of working that have been in place for decades. For the first time, capital can be used to rapidly scale up compute and focus it on a single problem, without the years of effort traditionally required to train people and build organizations. Agents are designed to follow instructions. Look at what just happened in mathematics. On September 8, OpenAI announced a solution to the Navier–Stokes existence and smoothness problem. The Hodge conjecture and the Riemann hypothesis could be next. A similar way of organizing work is emerging in cybersecurity. In cases recently disclosed by Anthropic, attackers were already using multiple subagents to divide up tasks such as reconnaissance, code review, and verification of findings. Offensive and defensive operations will increasingly move too fast and operate at too large a scale for human engineers to keep track of. This also gives frontier labs, with their vast amounts of compute and capital, something resembling a god’s-eye view of human society. In these fields, it is important to slow the pace or find ways to work with existing systems—even if every industry will eventually have to go through the transformation coding has experienced over the past two years. Yet many industries still lack the data needed for reinforcement learning, particularly workflow traces and action logs. Coding, mathematics, and cybersecurity are fields where data is relatively accessible and feedback is often readily available. In other industries, even a standardized context layer is missing. Take finance. You have the questions you ask GPT every day and the notes you keep in Notion. But much of an analyst’s work happens in the process of conducting research, deciding how and why to revise an EPS model, and reaching a conclusion. Analysts have long lacked—and still lack—the infrastructure to systematically capture these decision-making traces the way software engineers can record and revisit their work. We’ve spent a long time tackling this problem while building FUNDA’s own context layer. One reason coding can support progress toward recursive self-improvement, or RSI, is the abundance of accessible, high-quality data that can be reviewed and traced back to its source. In most other fields, we first need to address the data bottleneck. This challenge extends beyond frontier models: the robotics field has been grappling with similar issues for years. You can’t assume that every industry will compress five to ten years of progress into two years, as coding has. When we discuss regulation and slowing down AI, we’re dealing with several different realities: -In some fields, LLMs have already reached the limits of human capabilities. -In others, substantial work remains to label data and change everyday work habits. -Regulators and researchers who take a god’s-eye view, using the most advanced fields as their reference point, see enormous threats and feel a need to push back. -Practitioners looking at the data available in their own industries, the capabilities of AI, and the ROI see a much longer road ahead. They see a need for a society-wide effort to build the foundations for reinforcement learning, as happened in coding, before the next wave of rapid progress becomes possible. So even if we slow down, we should do so sector by sector, in stages. We should pay attention to the fields where AI is moving fastest, but also to the many industries still struggling to catch up. Ideally, we would put more effort into helping those industries advance, rather than slowing everyone down to the same pace. Beyond the differences between fields, coordination is another major challenge. The debates over distillation and model-routing platforms that have dominated the past month already show how difficult it is for frontier labs to stay in sync. Anthropic can cut off model-routing platforms access that threatens its business or its lead in model capabilities. But that spending could then shift to OpenAI. The biggest challenge in trying to plan social progress from a god’s-eye view is that a market economy won’t simply follow a plan. Competition and commercial interests will always shape the outcome. We already live in a society where the gap between ordinary people and AI researchers is widening. A more detailed plan, tailored to each sector and implemented in stages, would do more to earn public trust. Cooperation among the labs today could be the first step.

Deep| $BE : The Debate Has Shifted from AIDC Demand to Delivery Execution AIDC power scarcity remains the structural driver. We estimate North America faces a ~15GW AI power shortfall in 2027, as grid interconnection, turbine lead times, and downstream power infrastructure constrain how much announced capacity can actually be energized. This keeps time-to-power at a premium and supports Bloom’s behind-the-meter SOFC value proposition. The debate has shifted from demand to execution. Production slots appear substantially booked through 2028, but the market is increasingly focused on whether Bloom can convert headline capacity into shippable MW and site deliveries. We model ~2.7GW YE27 manufacturing capacity and ~2.2GW of 2027 deliveries vs. ~2.0GW consensus, with supplier ramp, testing, yield and service capacity becoming more relevant constraints. Scandium looks manageable near term, but service capacity risk is overlooked. Our channel checks suggest Bloom has diversified scandium sourcing across Malaysia, Japan and Canada, while current pricing does not indicate acute scarcity. The more overlooked risk is whether Bloom’s service organization can scale quickly enough to support a rapidly expanding multi-GW installed base. Bloom is more than a temporary gas-turbine shortage trade. We do not expect turbine supply to meaningfully normalize by 2030, extending Bloom’s time-to-power advantage. Longer term, modularity and native-DC compatibility with emerging 800V AIDC architectures could give SOFC a structural role even as conventional generation capacity expands. Backlog conversion is now the key rerating driver. Recent delays at Jupiter and Vineland look more like normal project-level timing risk than backlog impairment, but increasing customer concentration makes annual revenue more sensitive to permitting, construction and acceptance cadence. Following the July short reports, BE’s forward EV/EBITDA has reset from ~85x to ~42x; we see 2H26 delivery execution, 2027 guidance and progress on major AIDC projects as the key catalysts for Street estimates and the multiple to recover. Why now: Following a series of short reports in July, BE’s forward EV/EBITDA has reset from ~85x to ~42x. Despite a strong 26Q2 beat-and-raise, the stock has yet to regain momentum, suggesting the debate has shifted beyond the well-understood AIDC power demand. We see this as a good time to dig into the more important questions: how quickly Bloom can ramp capacity, deliver, and convert its AIDC backlog into revenue. Detailed Report fundaai.substack.com/p/deepb…

Introducing Korea Deleveraging-Pressure Dashboard. KOSPI has fallen sharply from its recent high, margin financing is contracting, and forced liquidations are running at extreme levels. The question for investors with Korea exposure is whether the deleveraging is complete or still in progress. This Play answers that question with daily-updated structural data. Deleveraging Pressure Index Nine sub-indicators synthesize into a single composite score that identifies the current phase of the deleveraging cycle. Three phases are distinguished: margin call trigger, forced liquidation dominance, and stabilization. The index updates daily as new KOFIA FreeSIS data arrives. Key Readings and Signals Credit margin balance, margin-to-deposit ratios, forced liquidation volumes, and index drawdown are tracked in a unified view. Each reading carries its historical percentile ranking and short-term momentum, making it possible to assess whether pressure is intensifying or fading. Signal Framework Three conditions must clear before a durable recovery: technical selling exhaustion, external catalyst resolution, and regulatory clarity. The Play monitors the first condition automatically and flags the remaining two for manual confirmation. Until all three signals turn green, rallies are classified as technical in nature. Historical Context Multi-year time series cover margin financing, forced liquidation volumes, outstanding receivables, and index levels across both KOSPI and KOSDAQ. Composite pressure history allows current readings to be compared against prior deleveraging episodes. Available to all paid clients on our full-stack research platform. Start here: funda.ai/agent-chat?play=sou…

Deep|LLM: RSI Is the Most Important Variable, and Compute Is the Deepest Moat July 2026 produced the sharpest drawdown of this cycle for the AI trade. Semiconductors posted their worst month since 2002, and more than $1 trillion of AI-linked market value came off. The concern behind the move is straightforward: coding looks like a special case that is hard to replicate, enterprise adoption is running behind expectations, and the “next coding” that would justify hundreds of billions of dollars of capex has not yet appeared. We think the debate is framed around the wrong question. The market is evaluating AI through an application lens, looking for the next killer use case and checking whether current revenue covers capital expenditure. The industry’s own objective, however, is AGI, and the mechanism most labs point to on that path is recursive self-improvement (RSI). A more useful way to think about it is as a sequence of capability milestones, each with its own commercial payoff. Coding capability has already been monetized, in the form of this year’s first-half revenue inflection. The next milestone is continual learning, which lets models accumulate knowledge in deployment the way an employee does; combined with deployment mechanisms such as forward-deployed engineering, it will open up most of the enterprise opportunity. Full RSI, in which models participate in and accelerate their own development, sits furthest out and will determine the competitive structure that follows. Progress is visible at each stage, and in several places it is accelerating. We make three claims. First, the “context problem” the market worries about is a capability question rather than a use-case question, and it is what continual learning is designed to address. Once models can accumulate tacit organizational knowledge the way employees do, the search for a single “next coding-like” use case dissolves: enterprise use cases open up progressively. The TAM reference then shifts from a software budget measured in hundreds of billions to a labor market measured in tens of trillions. Second, early signals of RSI are already observable. Model iteration has moved from annual to monthly. GPT-5.5 took roughly one month from the end of pre-training to release. OpenAI cut prices by up to 80% three weeks after GPT-5.6 shipped, against a history in which price cuts followed months of inference optimization. Lab code is now written almost entirely by models, and AI’s measured contribution to internal R&D velocity rose from 5% to 15–20% over six months. Third, if RSI crosses its threshold, catch-up strategies built on distilling frontier model outputs are likely to become progressively harder, and compute becomes the least compressible constraint and the deepest competitive moat. When the frontier iterates monthly, a follower may still hold a fixed time lag, but it cannot readily replicate a frontier lab’s full learning, data, and experimentation stack. Native self-improvement capability requires proprietary experimental infrastructure and the compute to run it. This is also the view inside China’s leading labs: China has not hit a scaling wall, but does not yet have enough compute to reach one. Capex, on this reading, should not be dismissed as a bubble. The latest earnings season shows real current returns from AI-driven cloud growth, and beyond that, capex is what ensures a player has sufficient compute when the RSI threshold arrives. Strategic necessity is not the same thing as shareholder return, and we treat that distinction explicitly in a scenario framework. The variable that matters most is less “what is the next application” than when RSI arrives and who gets there first. Detailed Report fundaai.substack.com/p/deepl…

Deep| $SPCX : Grok 4.5 Brings SpaceXAI Back at the Frontier-Lab Table; Remain Bullish on Harness Data Flywheels and Compute Demand What matters most about the Grok 4.5 release is that xAI / SpaceXAI is back at the table among North American frontier labs. At the beginning of the year, the North American frontier-model race still looked like a five-player game: OpenAI, Anthropic, Google, Meta, and xAI. But for a stretch, xAI had clearly fallen behind, and the market was more willing to treat it as a new neocloud, so the narrative shrank from five players to four. More recently, reports that Meta wanted to build “Meta Compute” and sell spare AI compute were read as frontier-lab consolidation and compute oversupply, bearish for neoclouds and AI infrastructure. Detailed Report fundaai.substack.com/p/deeps…

FUNDA now offers the Market Top and Bottom Indicator, a mid to long-term contrarian signal system. Low scores signal buy zones, high scores signal sell zones. Top and Bottom Score A 0 to 10 contrarian composite weighted across 11 sub-components, updated daily. Low readings correspond to fear, high readings to greed. As a high-conviction indicator, it triggers approximately 2 signals per year. Component Breakdown 11 dimensions scored on a 0 to 10 scale, covering credit spreads, yield curve, equity flows, bond flows, hedge fund positioning, CTA positioning, long-only positioning, sentiment, greed, equity breadth, and credit technicals. Each score reflects that dimension's current contrarian reading. Market Top and Bottom Indicator is available to all paid Substack subscribers. Start here: funda.ai/zh/agent-chat?play=…

Research| $META : Building a NeoCloud and Continuing to Rent Capacity are not Contradictory Not a contradiction: Meta considering external commercialization of surplus AI compute does not mean its AI compute demand has peaked. Meta is still locking in major new capacity, including roughly 1.6 GW from Crusoe data centers in Childress, Texas, and Warrenton, Missouri. Generational compute shift: Meta’s large H100/H200 fleet remains valuable for inference, fine-tuning, enterprise model serving, image/video generation, and traditional ML. But for 3T+ parameter MoE models, long context, multimodal training, and RL-heavy post-training, GB200/GB300 and future Vera Rubin systems offer better economics for frontier training. Tiered AI infrastructure: The AI compute market is moving from a single GPU shortage into multi-generation, tiered pricing and usage. GB300/Rubin-class systems remain scarce for frontier model training, while H100/H200 can shift toward inference, hosted models, agent workloads, and external compute monetization. Supply chain implication: Meta building a NeoCloud is not a bearish signal that demand has collapsed. It suggests GPU fleets are becoming financialized, multi-generation assets: older GPUs do not go to zero, and next-generation training compute remains scarce. Detailed Report fundaai.substack.com/p/resea…

Weekly|GLM 5.2, Memory Crowding Out Others, LLM Deep Dive, $MU Super-Cycle, $NBIS Re-Rating, Optics Moat Intact, Orbital DC Reality Check One of the questions that set the tone this week was whether China’s open-source models have started to hollow out frontier pricing power. We’d resist the framing. The recent risk-off across AI names traced a leverage unwind that rolled from Korea into Japan and then the US far more cleanly than it traced any GLM headline, and GLM has so far drawn a fraction of the hedge-fund attention DeepSeek once commanded. We also won’t wave the model away. On our internal data, GLM 5.2 lands around Opus 4.6, with a few benchmarks closer to 4.7–4.8, and the gap that actually matters has quietly compressed as third-party token routers turn cleaned inference traffic into RL datasets that open-source labs can finally buy. The cadence still reads as roughly six months behind the frontier, inside the market’s six-to-nine-month consensus, yet “good enough for the agent workload” is becoming the operative phrase for a widening set of tasks. A more important narrative shift drew heavy market attention this week: rising memory prices have begun to squeeze the margins of other semiconductor and hardware names, including AI beneficiaries. The market ran this logic once before, in 4Q last year, but it stayed contained within consumer electronics. This time, it has spread across AI, semi, and hardware beyond memory itself. We still see the trend in LLM ARR growth as the variable that sets market confidence. Yet regulation is keeping the latest Anthropic and OpenAI models off the market, Meta and Google have been slow to show clear progress, and Chinese open-source models keep pressing in. On balance, we think the AI trade, which has run strong for months now, sits in a fragile window, and investors should take note. We will keep tracking both the LLM side and the supply chain to read where the sector turns next. This Week’s Reports H1 2026 LLM Update (Parts 1 & 2). Our latest read on the model layer: the Agent Scaling Law is the real story, and token maxxing has moved into its ROI-discipline phase. Part 1 frames the industry dynamics and why a token-price pullback doesn’t mean peak AI demand; Part 2 goes lab by lab across the frontier and Chinese model vendors, landing on compute as the binding constraint of the value chain. fundaai.substack.com/p/deepl… fundaai.substack.com/p/deepl… Orbital Data Centers. Lower launch costs get the headlines, yet the orbital data-center case lives or dies on heat rejection, asset life, and replacement economics. We lay out why GW-scale stays a distant option and which engineering milestones actually de-risk it. fundaai.substack.com/p/deeps… Premium Report Snapshot - Institutional Report Review|MU FY26Q3: Clearing a High Bar; Memory Super Cycle Endures Micron printed $41.5bn revenue and $25.11 EPS against buy-side at $38.1bn / $22.38, and guided the August quarter above. The memory super-cycle keeps building, and the print does little to argue otherwise. Deep|NBIS: From Scarce GPU Capacity to Full-Stack AI Cloud Optionality An updated Nebius deep dive that reframes the story from scarce GPU supply to a full-stack AI cloud, with Token Factory and utilization as the re-rating levers, and stronger open-source models as a tailwind for token-factory demand. Deep|LITE & COHR: China Laser Capacity Doesn’t Dent the High-End AI Optics Moat Channel concern over second-tier Chinese laser and InP capacity is overdone; the high-end AI optics moat at Lumentum and Coherent holds because the roadmap moves too fast for mature-node-style commoditization. Detailed Report fundaai.substack.com/p/weekl…